Multi-measurement Target Tracking by Using Random Sampling Approach

In multi-target tracking field, conventional algorithms supposed that target is a point source and produces at most one measurement. While with the development of modern sensor technology, a target may give multiple measurements. In this paper, we consider that targets have certain geometrical shapes and give multiple measurements and call these targets multi-measurement targets (MmTs). We first build rigid models for the targets in parameter space and then estimate their parameters using the Markov chain sampling approach. Next, we derive the moving state described by target's centroid with our proposed equivalent measurement. When the number of targets remains unknown, under the Poisson assumption, we use the ratios of Poisson intensities to estimate the number of targets. We also define the probabilistic vectors of type (PVoT) and propose a recursive process for the PVoT. To verify the proposed algorithm, the final experiment proposes three targets, with different shapes and distributions, moving in a 2-dimension plane with constant velocity (CV). The experimental results show that the estimation of target state has an excellent precision and the shape estimation can better and stably reflect the change of target shape. Besides, the target lost rate is around 1.4% in 500 Monte Carlo (MC) runs.